namesrisk<-c(“Blue shark bycatch”,“Blue sharks”,“Sea lions”,“Leatherbacks”,“Swordfish”)
scenario A–> isolating the effect of swordfish weightings (5 runs, weightings are the same for both algorithms)
weightings <-c(0,0,0,0,0.1) #run A.1
weightings <-c(0,0,0,0,0.3) #run A.2
weightings <-c(0,0,0,0,0.5) #run A.3
weightings <-c(0,0,0,0,0.7) #run A.4
weightings <-c(0,0,0,0,0.9) #run A.5
scenario B–> isolating the effect of leatherback weightings (5 runs, weightings are the same for both algorithms)
weightings <-c(0,0,0,-0.1,0) #run B.1
weightings <-c(0,0,0,-0.3,0) #run B.2
weightings <-c(0,0,0,-0.5,0) #run B.3
weightings <-c(0,0,0,-0.7,0) #run B.4
weightings <-c(0,0,0,-0.9,0) #run B.5
scenario C–> testing the ability to manage swordfish and leatherback equally (5 runs, weightings are the same for both algorithms)
weightings <-c(0,0,0,-0.1,0.1) #run C.1
weightings <-c(0,0,0,-0.3,0.3) #run C.2
weightings <-c(0,0,0,-0.5,0.5) #run C.3
weightings <-c(0,0,0,-0.7,0.7) #run C.4
weightings <-c(0,0,0,-0.9,0.9) #run C.5
scenario D–> testing the ability to manage swordfish and leatherback, tailored based on above results (5 runs, weightings are the same for both algorithms)
C.3 was the best for Marxan, the Bs were the best for EcoROMS. Seems like its best when lbst is greater than swor
weightings <-c(0,0,0,-0.5,0.1) #run D.1
weightings <-c(0,0,0,-0.5,0.3) #run D.2
weightings <-c(0,0,0,-0.7,0.1) #run D.3
weightings <-c(0,0,0,-0.7,0.3) #run D.4
weightings <-c(0,0,0,-0.7,0.5) #run D.5
scenario E–> testing the ability to manage swordfish and leatherback, tailored based on above results, swor > lbst (5 runs, weightings are the same for both algorithms)
in the orginial analysis, this config should make marxan work better
weightings <-c(0,0,0,-0.3,0.7) #run E.1
weightings <-c(0,0,0,-0.3,0.9) #run E.2
weightings <-c(0,0,0,-0.5,0.7) #run E.3
weightings <-c(0,0,0,-0.5,0.9) #run E.4
weightings <-c(0,0,0,-0.7,0.9) #run E.5